# Long non-coding RNA signatures to track treatment responses in multiple sclerosis

> **NIH NIH R44** · IQUITY LABS, INC · 2020 · $499,995

## Abstract

ABSTRACT
 Early detection of multiple sclerosis is key to limiting neurological damage but monitoring
patient progression and response to therapy is of arguably similar if not greater importance due
to the chronic nature of disease. Moreover, rates of non-adherence to therapy has been reported
to be as high as 25% to 40% in the patient population suggesting the need to provide continuous
monitoring and selection of optimal therapy. Identification of novel actionable biomarkers would
provide clinicians with additional information for the purposes of diagnosis, prognosis, clinical
subtyping as well as for the selection and monitoring of therapy. Initiation of sub-optimal therapy
can be both detrimental to the patient’s health and financial well-being.
 To date, the general approach to selecting a disease modifying treatment (DMT) is to weigh
the risks and benefits while considering the aggressiveness of disease, efficacy of the drug and
the potential side effects of treatment in a “trial and error” fashion. This approach is quite unsettling
when understanding that treatment failure or inadequacy can cause irreversible neurological
damage. Furthermore, many of these drugs are associated with serious adverse drug reactions
such as cardiac events, opportunistic infections and secondary autoimmunity. Selection of the
best therapy for a particular patient as well as the ability to identify if/when efficacy of a particular
DMT dwindles is highly desirable and would be of great benefit throughout the healthcare
spectrum. The course of MS disease does not manifest identically in all patients nor do all patients
respond to treatment the same way. Identification of actionable biomarkers to serve as a
surrogate for the efficacy of a particular therapy would allow clinicians to identify nonresponsive
patients as early as possible and potentially evaluate dosing or administration to optimize patient
outcomes.
 Our previous work has explored lncRNAs as candidate biomarkers that can be measured in
peripheral whole blood to accurately classify MS. The preliminary data provided in support of our
fast track application highlights the potential for lncRNA expression levels analyzed with machine
learning to not only classify MS but also indicate treatment responses.

## Key facts

- **NIH application ID:** 10088013
- **Project number:** 4R44AI145505-02
- **Recipient organization:** IQUITY LABS, INC
- **Principal Investigator:** Charles Floyd Spurlock
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $499,995
- **Award type:** 4N
- **Project period:** 2020-03-10 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10088013

## Citation

> US National Institutes of Health, RePORTER application 10088013, Long non-coding RNA signatures to track treatment responses in multiple sclerosis (4R44AI145505-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10088013. Licensed CC0.

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